simple speed sensorless dtc-svm scheme for induction motor … · 2020. 5. 19. · key words: speed...

BULLETIN OF THE POLISH ACADEMY OF SCIENCES

TECHNICAL SCIENCES, Vol. 61, No. 2, 2013

DOI: 10.2478/bpasts-2013-0028

Simple speed sensorless DTC-SVM scheme

for induction motor drives

H. ABU-RUB1, D. STANDO2∗, and M.P. KAZMIERKOWSKI3

1 Texas A&M University at Qatar, Doha 23874, Qatar2 Electrotechnical Institute, 28 Pożaryskiego St., 04-703 Warsaw, Poland

3 Institute of Control and Industrial Electronics, Warsaw University of Technology, 75 Koszykowa St., 00-662 Warsaw, Poland

Abstract. The paper focuses on the development of a novel DSP based high performance speed sensorless control scheme for PWM voltage

source inverter fed induction motor drives. Firstly, two generic torque and flux control methods the Field Oriented Control (FOC) and

Direct Torque Control (DTC), are briefly described. For implementation the sensorless scheme DTC with Space Vector Modulation (DTC-

SVM) has been selected because it eliminates the disadvantages associated with the DTC while keeping the advantages of both FOC and

DTC. Secondly, the simple flux vector observer allowing speed sensor elimination is given. The novelty of the presented system lays in

combining the DTC-SVM structure with a simple observer for both torque/flux and speed sensorless control. Furthermore, the DTC-SVM

structure which operates in speed sensorless and torque control mode is presented. Finally, the description of a 50 kW laboratory drive and

experimental results illustrating properties of the system are given.

Key words: speed sensorless control, pulse width modulated (PWM) voltage source inverters, induction motor drives, direct torque control

(DTC), DTC-SVM, adjustable speed drives (ASD).

1. Introduction

The converter-fed adjustable speed drives (ASD) with induc-

tion motor (IM) are widely used in industry and transporta-

tion systems. In the last decade, several techniques are de-

veloped which allow for elimination of motion (speed or po-

sition) shaft sensor of IM drives while keeping enough pre-

cision and high dynamic performance. The techniques used

for speed/position elimination are known in the literature as

sensorless or encoderless [1–4]. Among the main advantages

of sensorless controlled drives there are:

• Lower cost,

• Reduced hardware complexity,

• Reduced size of the drive,

• Elimination of the sensor cables,

• Higher noise immunity,

• Lower maintenance requirements,

• Possible operation in aggressive environments,

• Reliable, and user friendly operation.

The basic principles used for speed/position estimation

(observation) can be classified into [1, 3, 4]: speed estima-

tors, model reference adaptive system (MRAS), adaptive ob-

servers, Kalman filters, rotor slot ripple. All these methods,

except rotor slot ripple, are based on flux vector observers for

speed estimation.

In this work the development and investigation of the sim-

ple speed sensorless vector controlled IM drive which can

operate in both torque or speed control modes is presented.

In the first part, the paper discusses torque and flux control

methods and for practical implementation the DTC with Space

Vector Modulation (DTC-SVM) has been chosen because it

eliminates the traditional DTC disadvantages while keeping

the advantages of both classical FOC and DTC schemes. Fur-

ther, the simple flux vector observer allowing speed sensor

elimination is presented. The novelty of the presented sys-

tem consists in combining the universal DTC-SVM structure

with a simple observer for both torque/flux and speed sensor-

less control. Finally, the description of the 50 kW laboratory

drive system with DSP based control and estimation as well

as experimental results illustrating properties of the developed

system are given.

2. Control schemes

2.1. Complex space vector based equation of induction

motor (IM). Mathematical description of the three-phase IM

is based on complex space vectors, which are defined in

the coordinate system rotating with the synchronous angu-

lar speed Ωs. In absolute-units and real-time representation

the following equations describe a behaviour of the idealized

cage-rotor IM [2–5]:

Vs = RsIs +dΨs

dt+ jΩKΨs, (1)

0 = RrIr +dΨs

dt+ j(ΩK − pbΩm)Ψr, (2)

Ψs = LsIs + LMIr, (3)

Ψr = LrIr + LMIs, (4)

dΩm

dt=

1

J(Te − TL). (5)

∗e-mail: [email protected]

301

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H. Abu-Rub, D. Stando, and M.P. Kazmierkowski

The electromagnetic torque Te can be expressed by the

following formula:

Te = pb

3

2Im(Ψs ∗ Is), (6)

where Ir – rotor current space vector, Is – stator current space

vector, J – moment of inertia, LM – main, magnetizing induc-

tance, Ls – stator winding self-inductance, Lr – rotor wind-

ing self-inductance, Te – electromagnetic torque, TL – load

torque, pb – number of pole pairs, Rr – rotor phase windings

resistance, Rs – stator phase windings resistance, Vs – stator

voltage space vector, Ψs – vector of the stator flux linkage,

Ψr – vector of the rotor flux linkage, ΩK – angular speed of

the coordinate system, Ωm – angular speed of the IM shaft.

2.2. General block scheme of speed controlled IM drive.

The general block scheme of high performance speed con-

trolled induction motor drive is shown in Fig. 1. The core of

the scheme are inner flux and torque control loops with the

estimator block which can be implemented in different ways,

whereas the outer speed control loop is rather unified and gen-

erates command values for torque Tc and flux |Ψ|c (via Flux

program block) controllers. The speed feedback signal can be

measured by a mechanical motion (speed/position) sensor Ωm

or calculated in the estimator Ωm creating possibility of the

motion sensorless operation.

Fig. 1. General block scheme of speed controlled induction motor

drive

2.3. Selection of torque and flux control methods. Sever-

al basic Torque Control (TC) methods have been developed

in the last decades [7]. Not all of them have found wide in-

dustrial applications. Therefore, we present only most popular

strategies used commercially.

Field Oriented Control (FOC). The proposed in 1970-ties by

Hasse [8] and Blaschke [9] FOC method is based on an anal-

ogy to the DC brush motor. In this motor, owing to separate

exciting and armature windings, flux is controlled by exciting

current and torque is controlled independently by adjusting the

armature current. So, the flux and torque currents are elec-

trically and magnetically separated. Contrarily, the cage-rotor

IM has only a three-phase winding in the stator, and the stator

current vector, Is, is used for both flux and torque control. So,

exciting and armature current are coupled (not separated) in

the stator current vector and cannot be controlled separately.

The decoupling can be achieved by the decomposition of the

instantaneous stator current vector, Is, into two components:

flux – producing current, isd, and torque-producing current,

isq, in the rotor-flux-oriented coordinates (R-FOC) dq (see

vector diagram in Fig. 2). In this way, the control of the IM

becomes identical with a separately excited DC brush motor

and can be implemented using a current controlled PWM in-

verter with linear PI controllers and voltage SVM (see block

scheme in Fig. 2). The core of the FOC scheme are coor-

dinate transformation blocks which allow calculation of field

oriented current components isd, isq by using αβ/dq transfor-

mation, and reference voltage vector components vsαc, vsβc

by using dq/αβ transformation. So, in the FOC scheme torque

and flux are controlled indirectly by field oriented current vec-

tor components.

Fig. 2. Vector diagram and block scheme of rotor FOC. Torque and

flux are controlled indirectly via torque current isq and flux current

isd control loops

Switching Table based – DTC Scheme (ST-DTC). The

block diagram of the ST-DTC scheme proposed by Takahashi

and Noguchi [10] is shown in Fig. 3. The stator flux magni-

tude |Ψ|sc and the motor torque Tc are the command signals

which are compared with the estimated

∣∣∣Ψ∣∣∣s

and Te values,

respectively. The digitized flux and torque errors generated

by the hysteresis controllers dΨ, dT and the position sector

N(γs) of the stator flux vector obtained from the angular po-

sition γs = arctg(Ψsβ/Ψsα) selects the appropriate voltage

vector from the switching selection table. Thus, pulses SA,

SB , SC for control the inverter power switches are generated

from the vector selection table.

The characteristic features of the ST-DTC scheme of Fig. 3

include:

• Sinusoidal stator flux and current waveforms with harmon-

ic content determined by the flux and torque controller

hysteresis tolerance bands,

302 Bull. Pol. Ac.: Tech. 61(2) 2013



Simple speed sensorless DTC-SVM scheme for induction motor drives

• Excellent torque dynamics (depending on voltage reserve),

• Flux and torque hysteresis bands determine the inverter

switching frequency, which varies with the synchronous

speed and load changes.

Compared to the conventional FOC (Fig. 2), the DTC has

the following features:

• Simple structure,

• There is no current control loops; hence, the current is not

regulated directly;

• Coordinate transformation is not required,

• There is no separate voltage pulse width modulator (PWM),

• Accurate stator flux vector and torque estimation is re-

quired.

Fig. 3. Vector diagram and block scheme of Switching Table based

DTC. Torque and flux are controlled directly by selection of appro-

priate forward/backward active inverter voltage vector (V1 or V6) and

stops by selection zero voltage vector V0. Stator flux vector moves

on circular path

Direct Torque Control with Space Vector Modulation

(DTC-SVM). Many modifications of the classical ST-DTC

scheme aimed at improving starting, very low speed oper-

ation, torque ripple reduction, overload conditions, variable

switching frequency functioning, and noise level attenuation

have been proposed during last decade [11]. One of the so-

lutions is the DTC-SVM with closed-loop torque and flux

control operating in Cartesian stator flux coordinates (Fig. 4)

[1, 6, 11]. The output of the PI flux and torque controllers is

interpreted as the reference stator voltage component, vΨc and

vTc, in stator flux oriented (S-FOC) (dq) coordinates. These

DC voltage commands are then transformed into stationary

coordinates (αβ), and the commanded values, vsαc and vsβc,

are delivered to the SVM block. Note that this scheme can be

seen as simplified S-FOC without current control loops [12]

or as classical ST-DTC scheme (see [10]) in which switching

table is replaced by modulator (SVM) and hysteresis torque

and flux controllers are replaced by linear PI [1, 6, 13]. So, in

the DTC-SVM scheme torque and flux are controlled directly

in closed loops, and therefore an accurate estimation of motor

flux and torque is necessary. Differently from the nonlinear

DTC scheme where signals are processed on instantaneous

values, in the linear DTC-SVM scheme, the linear (PI) con-

trollers operate on values averaged over the sampling period.

Therefore, the sampling frequency can be reduced from about

40 kHz required in nonlinear DTC, to 2–5 kHz in linear DTC-

SVM scheme. Also, operation at constant switching frequency

improves considerably the drive performance in terms of re-

duced torque and flux pulsations, reliable start-up and low

speed operation.

Fig. 4. Vector diagram and block scheme of the implemented DTC-

SVM. Torque and flux are controlled directly via stator voltage vector

components vMc and vΨc

Table 1 summarizes features of described torque and flux

control methods. It can be seen that DTC-SVM is a combina-

tion of DTC and FOC which eliminates basic disadvantages

while keeping main advantages of both methods. Therefore,

the DTC-SVM scheme has been selected for implementation

of speed sensorless IM drive.

Bull. Pol. Ac.: Tech. 61(2) 2013 303




Table 1

Comparison of control methods

FOC DTC DTC-SVM

Advantages

⋆ PWM Modulator

⋆ Constant switching frequency

⋆ Unipolar inverter output voltage

⋆ Low switching losses

⋆ Low sampling frequency

⋆ Current control loops are required

X Structure independent of rotor pa-

rameters, universal for IM and

PMSM

X Simple implementation of sensor-

less operation

X No coordinate transformation

X No current control loops

X Structure independent of rotor pa-

rameters, universal for IM and

PMSM

X Simple implementation of sensor-

less operation

X No coordinate transformation

X No current control loops

⋆ PWM Modulator

Constant switching frequency

⋆ Unipolar inverter output voltage

⋆ Low switching losses

Low sampling frequency

Disadvantages

• Coordinate transformation are re-

quired

• Multi-loop control

• Control structure depended on ro-

tor parameters

• No PWM modulator

• Bipolar inverter output voltage

(higher switching losses)

• Variable switching frequency

• High switching losses

• High sampling frequency

3. Flux vector and angular speed estimation

Implementation of any high performance drive system re-

quires a high accuracy estimation of the actual stator or/and

rotor flux vector (magnitude and position) and electromag-

netic torque. Once the flux vector is accurately estimated,

the torque estimation is performed easily as a cross product

of the flux and measured stator current vectors. Also, there

is a strong trend to avoid AC voltage sensors and mechan-

ical motion (speed/position) sensors because it reduces cost

and improves reliability and functionality of the drive system.

A good review of IM speed sensorless control schemes is

presented in [4, 12, 14].

3.1. Flux Vector Estimation. To avoid the use of flux sen-

sors or measuring coils in the IM, methods of indirect flux

vector generation have been developed, known as flux models

or flux estimators. These are models of IM equations which

are excited by appropriate easily measurable quantities, such

as stator voltages and/or currents (Vs, Is), angular shaft speed

(Ωm) or position angle (γm). There are many types of flux

vector models, which usually are classified in terms of the in-

put signals used [3–5]. Such models generate the stator or/and

rotor flux vector which, in an ideal case, rotates synchronous-

ly with the IM magnetic field. Because the IM parameters

very often are known only roughly, and change with operating

point and temperature, therefore an error appears between the

actual IM flux and that estimated in the used model. The er-

ror depends on: model variants, parameter deviation between

IM and model, accuracy of input signal measurement, motor

point of operation. To minimize the sensitivity of flux mod-

els to motor parameters variation, use is made of the model

adaptive reference systems (MARS) and the observer tech-

nique [15, 16]. Also, sliding mode approach for robust flux

estimation has been proposed [17].

In this work a simple stator flux vector observer operat-

ing without speed/position signal has been implemented. The

observer equations (7)–(10) are derived from the IM space

vector Eqs. (1)–(4), and are expressed as [18, 19]:

dΨIs

dt=

(−Ψ

Is +

LM

Lr

Ψr

)Rs

σLs

+Vs −K(Is − Is

), (7)

dΨIIs

dt= Vs − RsIs, (8)

Ψr =(Ψ

IIs − σLsIs

) Lr

LM

, (9)

Is =

(Ψ

Is −

LM

Lr

Ψr

)/σLs. (10)

The correction term K(Is − Is) existing in the observer

equation (7) is calculated as an error between the currents:

measured Is and estimated Is in equation (10). This error

term is multiplied by gain factor K allowing compensation

of a drift and parameter changes. This K factor was tuned

according to a discussion conducted in [19]. The rotor flux

vector is calculated using the equation (9).

3.2. Speed estimation. Estimated rotor flux vector Ψr and

measured stator currents, allow to calculate the IM mechanical

angular speed as:

Ωr = Ωs −LM

Tr

Ψrαisβ − Ψrβisα∣∣∣Ψr

∣∣∣2

, (11)

where Tr – rotor time constant, isα and isβ – current vector

components in the stationary α − β coordinates.

The block diagram of the implemented stator flux vec-

tor and angular speed estimation according to Eqs. (7)–(10)

and (11) is shown in Fig. 5.

304 Bull. Pol. Ac.: Tech. 61(2) 2013




Fig. 5. Block diagram of flux observer and speed calculation struc-

ture

4. Description of the laboratory drive system

The laboratory setup consists of two identical 50 kW induc-

tion motors each supplied by back-to-back AC-DC-AC volt-

age source power converters (Fig. 6). So, the converter system

allows for motor and generator mode of operation. The pa-

rameter specification of the IM and converters are given in

Table 2.

Fig. 6. Block scheme of laboratory setup

Table 2

Laboratory setup specification

IM type STDA 200LU

PN 50 kW Rs 64.5 mΩ

VN 3 × 380 Rr 46.3 mΩ

IN 88 A Ls 25.217 mH

fN 65 Hz Lr 25.137 mH

TeN 249 Nm LM 24.75 mH

ΩN 1917 rpm J 10 kg·m2

Power converter AC/DC and DC/AC

PN 55 kW

IN 98 A

VN 3 × 400 V 50 Hz

fimp 4 kHz

The proposed DTC-SVM scheme was implemented in a

dSpace 1103 platform and some auxiliary circuits were used.

The conditioning interface includes: conditioning current sig-

nal from LEM sensors to voltage in appropriate range for

dSpace card, over current and over voltage protection, and

also fibre optics link for IGBT power transistors.

5. Real-time implementation of control

and estimation algorithms

The whole control and estimation algorithm have been im-

plemented in C language using ControlDesk software provid-

ed by dSpace [20, 21]. The algorithm structure is shown in

Fig. 7. The main parts are: estimation and DTC-SVM block.

The estimation block contains Eqs. (7)–(11) and Fourth-Order

Runge-Kuta Method (RK4). The RK4 integration method re-

quires large amount of calculations, more than e.g. the Euler

method, but it is more precise, giving higher stability of the

estimator. The DTC-SVM block contains a control structure

consisting of linear PI regulators and a space vector modula-

tor.

Fig. 7. Block scheme of the implemented estimation and DTC-SVM

algorithm

The control action of the whole system is synchronized

with the SVM-generation and executed with sampling time

250 µs equal to the inverter switching time.

6. Experimental results

The presented algorithm has been investigated experimentally

in steady states and dynamic operation for different IM speeds

and loads.

Steady state performances. At first, the accuracy of the speed

estimation in region of the reference speeds 10–1100 rpm, and

Bull. Pol. Ac.: Tech. 61(2) 2013 305




load torque of 100 Nm and 200 Nm have been investigated

and results are summarized in Table 3. As it can be seen, the

speed error is within the range of 2.6–3.76 rpm and for the

load torque 200 Nm is approximately two times higher than

for 100 Nm.

Table 3

Speed estimation errors for different speed and load torque values

Tload

100 Nm 200 Nm

Ωm [rpm] ∆Ωm [rpm] ∆Ωm [rpm]

1100 3.76 7.7

700 3.6 7.4

300 3.6 7.2

100 3.4 6.8

50 3.3 5.7

40 3 5.7

30 2.6 5.4

15 2.7 5.5

10 2.7 5.3

Secondly, the operation of the IM speed sensorless drive

in steady state at speed 1100 rpm and load torque 200 Nm

has been illustrated in oscillograms of Fig. 8.

Fig. 8. Steady state operation for 1100 rpm speed and 200 Nm load

torque (bΩm – estimated speed, bTe – estimated torque, isa – phase

current)

Dynamic performances. Some selected results of dynamic

tests are presented in the oscillograms of Figs. 9–12. The ex-

cellent torque tracking performance in torque control mode

(open speed loop) is shown in Fig. 9. Similarly, in Fig. 10

speed reversal under torque control mode ±150 Nm is shown.

The speed tracking performances are presented in Fig. 11

for speed reference changes: 50 rpm – 900 rpm – 50 rpm.

As it can be seen the averaged speed error stays within the

range of 5 rpm during the transients (acceleration and de-

acceleration) and around zero for a steady state operation at

speed 900 rpm.

Fig. 9. Operation in torque control mode: torque reference changes

±100 Nm (bΩm – estimated speed, bTe – estimated torque, isa – phase

current)

Fig. 10. Speed reversal for torque changes ±150 Nm (bΩm – es-

timated speed, bTe – estimated torque, isa – phase current, bΨsα –

estimated stator flux)

Fig. 11. Speed tracking performance for reference changes 50–

900–50 rpm (Ωm – speed from a sensor, bΩm – estimated speed,

∆Ωm = Ωm − bΩm)

306 Bull. Pol. Ac.: Tech. 61(2) 2013




Fig. 12. Response of the speed control loop to step change of the

load torque from 0 Nm to 200 Nm, for 400 rpm reference speed

(bΩm – estimated speed, bTe – estimated torque, isa – phase current)

Finally, the performance of the speed stabilization loop

under step change of the load torque: 0–200 Nm – 0 is pre-

sented in Fig. 12. Note that electromagnetic torque and stator

phase current change very fast without any oscillations.

7. Conclusions

In this paper a simple algorithm for fast stator flux and speed

estimation is presented and implemented in 50 kW PWM in-

verter fed-induction motor (IM) drive. The speed sensorless

drive operates in direct torque control with the space vec-

tor modulation (DTC-SVM) scheme presented in Fig. 4. The

flux vector observer is based on a stator voltage equation and

simple algebraic flux-current equations without speed/position

signals. In spite of simplicity, the drive can operate in torque

or speed control modes, and guarantees a proper dynamic

performance and the moderate speed estimation accuracy in

steady states.

Acknowledgements. The NPRP grant (NPRP 4-077-2-028)

from the Qatar National Research Fund (a member of The

Qatar Foundation) made this publication possible. The state-

ments included herein are solely the responsibility of the au-

thors.

REFERENCES

[1] I. Boldea and S.A. Nasar, Electric Drives, CRC Press, Boca

Raton, 2006.

[2] B.K. Bose, “Power electronics and motor drives recent

progress and perspective”, IEEE Trans. on Ind. Electronics 56

(2), 581–588 (2009).

[3] P. Vas, Sensorless Vector and Direct Torque Control, Claren-

don Press, Oxford, 1998.

[4] M. Cirrincione, M. Pucci, and G. Vitale, Power Converters

and AC Electrical Drives with Linear Neural Networks, CRC

Press, Boca Raton, 2012.

[5] M.P. Kazmierkowski and H. Tunia, Automatic Control of Con-

verter Fed Drives, Elsevier, Amsterdam, 1994.

[6] L. Xu and M. Fu “A sensorless direct torque control technique

for permanent magnet synchronous motors”, IEEE Industrial

Applications Conf. 1, 159–164 (1999).

[7] M.P. Kazmierkowski, L.G. Franquelo, J. Rodriguez, M. Perez,

and J.I. Leon, “High-performance motor drives”, IEEE Indus-

trial Electronics Magazine 5 (4), 6–26 (2011).

[8] K. Hasse, “Drehzahlgelverfahren fur schnelle Umkehrantriebe

mit stromrichtergespeisten Asynchron – Kurzchlusslaufermo-

toren”, Reglungstechnik 20, 60–66 (1972).

[9] F. Blaschke, “The principle of field-orientation as applied to

the transvector closed-loop control system for rotating-field

machines”, Siemens Review 34, 217–220 (1972).

[10] I. Takahashi and T. Noguchi, “A new quick-response and high

efficiency control strategy of an induction machine”, IEEE

Trans. on Industrial Application IA-22 (5), 820–827 (1986).

[11] G.S. Buja and M.P. Kazmierkowski, “Direct torque control of

PWM inverter-fed AC motors – a survey”, IEEE Trans. on Ind.

Electronics 51 (4), 744–757 (2004).

[12] J. Holtz, “Sensorless control of induction machines—with or

without signal injection?”, IEEE Trans. on Ind. Electron. 53

(1), 7–30 (2006).

[13] D. Casadei, F. Profumo, G. Serra, and A. Tani, “FOC and DTC:

two viable schemes for induction motors torque control”, IEEE

Trans. on Power Electronics 17 (5), 779–787 (2002).

[14] M. P. Kazmierkowski, R. Krishnan, and F. Blaabjerg, Control

in Power Electronics, Academic Press, London, 2002.

[15] T. Orłowska-Kowalska, “Application of extended Luenberger

observer for flux and rotor time-constant estimation in induc-

tion motor drives”, IEE Proceedings 136 (6), 324–330 (1989).

[16] R. Nilsen and M.P. Kazmierkowski, “Reduced order observer

with parameter adaption for fast rotor flux estimation in induc-

tion machines”, IEE Proc. D 136 (1), 35–43 (1989).

[17] T. Orłowska-Kowalska and M. Dybkowski, “Performance

analysis of the sensorless adaptive sliding-mode neuro-fuzzy

control of the induction motor drive with MRAS-type speed

estimator”, Bull. Pol. Ac.: Tech. 60 (1), 61–70 (2012).

[18] J. Guzinski and H. Abu-Rub, “Speed sensorless induction mo-

tor drive with predictive current controller”, IEEE Trans. on

Industrial Electronics 60 (2), 699–709 (2013).

[19] H. Abu-Rub, and J. Guzinski, “Simple observer for induc-

tion motor speed sensorless control”, Proc. 37th Annual Conf.

IEEE Industrial Electronics Society, IECON 2011 1, 2024–

2029 (2011).

[20] dSPACE, RTLib Reference, September 2001.

[21] dSPACE, RTI Reference, September 2001.

Bull. Pol. Ac.: Tech. 61(2) 2013 307



simple speed sensorless dtc-svm scheme for induction motor … · 2020. 5. 19. · key words: speed...

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